Automated Measurement of Bowel Damage Using Enterography Imaging to Predict Clinical Outcomes in Crohn’s Disease.

使用肠造影成像自动测量肠道损伤来预测克罗恩病的临床结果。

• 批准号: 10617199
• 负责人: Ryan William Stidham
• 金额: $ 52.6万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617199
• 关键词: Accounting; Affect; Agreement; American; Anti-Inflammatory Agents; Assessment tool; Biological; Biological Markers; Biological Response Modifier Therapy; Biopsy; Caring; Characteristics; Clinical; Colitis; Collection; Colon; Colonoscopy; Communities; Companions; Computer Analysis; Computers; Crohn' s disease; Data; Data Set; Decision Making; Development; Digestive System Disorders; Disease; Disease Outcome; Endoscopy; Excision; Fibrosis; Foundations; Goals; Health; Histologic; Histology; Histopathologic Grade; Hospitalization; Image; Image Analysis; Inflammation; Inflammatory; Interobserver Variability; Intestinal Fibrosis; Intestines; Laboratories; Laboratory Study; Length; Measurement; Measures; Mesentery; Methodology; Methods; Mission; Modeling; Monitor; Mucositis; Operative Surgical Procedures; Outcome; Pathologic; Patients; Performance; Pharmaceutical Preparations; Phenotype; Prediction of Response to Therapy; Predisposition; Prognosis; Public Health; Quality of life; Reproducibility; Research; Resected; Scanning; Schedule; Small Intestines; Standardization; Steroids; Surgical Models; Surveys; Symptoms; Techniques; Testing; Texture; Therapeutic; Therapeutic Trials; Thick; Time; Tissues; Treatment Efficacy; Treatment outcome; United States; United States National Institutes of Health; Work; X-Ray Computed Tomography; burden of illness; clinical care; clinical practice; community setting; disability; disease natural history; disease phenotype; economic cost; gut inflammation; high dimensionality; imaging biomarker; imaging modality; imaging study; improved; individual patient; innovation; novel; personalized care; personalized medicine; personalized predictions; predict clinical outcome; predictive modeling; prognostic; prospective; radiologist; recruit; risk stratification; small bowel Crohn' s disease; therapy outcome; treatment response

Current management of Crohn’s disease (CD) relies on monitoring objective endpoints of mucosal inflammation. While structural bowel damage drives surgery in more than half of patients with CD, assessments of structural bowel damage are challenging to quantify and incorporate into treatment decision- making. Cross-sectional imaging can survey deep bowel damage and fibrostenotic changes, but the time and expertise needed, and the susceptibility of qualitative features to interobserver variation, pose challenges in the broader use of imaging data to personalize care. The long-term goal of this research is to develop methods to objectively measure structural bowel damage and individualize predictions of clinical outcomes in CD. The overall objectives in this application are to test (i) the ability of computational image analysis methods to collect traditional and novel characterizations of bowel damage using common enterography imaging studies and (ii) to evaluate these measures’ ability to improve predictions of CD outcomes. The central hypothesis is that bowel damage features collected by computational image analysis methods will improve the accuracy of models predicting therapeutic and clinical outcomes in CD. This central hypothesis will be tested through three specific aims: (1) Determine the performance of computational analysis of enterography studies capturing bowel damage measurements for predicting CD clinical outcomes in the regular course of care, (2) Prospectively compare the performance of enterography image analysis for predicting therapeutic response to existing laboratory and endoscopic measures, and (3) Evaluate image analysis capacity to determine underlying tissue histology in CD using conventional imaging. In the first aim, enterography studies in a national prospective CD natural history dataset will undergo image analysis to extract measurements used to model surgical, hospitalization, and steroid use outcomes. Further work in this aim will test the agreement between expert radiologists and computer-derived bowel measurements. In the second aim, subjects starting new biologic therapies will undergo scheduled enterography to compare the prognostic capabilities of computationally derived bowel features to inflammatory biomarkers and endoscopy for predicting therapeutic response. Finally, in the third aim, patients undergoing elective surgical resection of intestine for CD will have pre-operative enterography. High dimensional image features will be used to model histologic grading of inflammation and fibrosis. The proposed research is innovative in approaching structural bowel damage as a related, but independent and equally important, companion assessment to inflammation in the prognosis and treatment of CD. Further, computational image analysis opens new horizons not only in objectivity and reproducibility, but also concepts of how to measure CD burden. The proposed research is significant because it will demonstrate the indispensable importance of structural intestinal damage features for the most accurate predictions of CD course and therapeutic responsiveness in both clinical care and therapeutic trials.

目前对克罗恩病(CD)的治疗依赖于监测粘膜的客观终点 发炎。虽然结构性肠道损伤促使超过一半的CD患者接受手术， 对结构性肠道损伤的评估具有挑战性，难以量化并纳入治疗决策- 制作。横断面成像可显示深部肠损伤和纤维狭窄改变，但时间和 所需的专门知识，以及定性特征对观察者间差异的敏感性，对 更广泛地使用成像数据来个性化护理。这项研究的长期目标是开发方法来 客观地测量结构性肠损伤，并对CD的临床结果进行个体化预测。这个 本应用程序的总体目标是测试(I)计算图像分析方法收集 肠损伤的传统和新的特征使用普通的肠造影学研究和(Ii) 评估这些措施改善CD结果预测的能力。中心假设是 通过计算图像分析方法收集肠道损伤特征将提高诊断的准确性 预测CD的治疗和临床结果的模型。这一核心假设将通过三个方面进行检验 具体目标：(1)确定肠道造影术研究捕获的计算分析的性能 在常规护理过程中用于预测CD临床结果的肠道损伤测量(2) 前瞻性比较肠道造影术图像分析在预测治疗反应方面的性能 现有的实验室和内窥镜检查措施，以及(3)评估图像分析能力以确定 使用常规成像的CD中的潜在组织组织学。在第一个目标中，肠道造影术研究 国家预期CD自然历史数据集将进行图像分析，以提取用于 模拟手术、住院和类固醇使用结果。这一目标的进一步工作将考验该协议 专家放射科医生和计算机得出的肠道测量之间的差异。在第二个目标中，受试者从 新的生物疗法将接受预定的肠道造影术，以比较患者的预后能力 计算得出的肠道特征与炎性生物标志物和内窥镜检查预测疗效的关系 回应。最后，在第三个目标中，接受CD的择期手术切除的患者将有 术前肠道造影术。高维图像特征将被用来建模组织学分级 炎症和纤维化。拟议中的研究在将结构性肠道损伤作为一种 与炎症在预后和预后中的相关性，但独立的和同等重要的伴随评估 CD的治疗。此外，计算图像分析不仅在客观性和 可再现性，但也是如何衡量CD负担的概念。这项拟议的研究具有重要意义，因为 它将证明结构性肠道损伤特征对于最准确的 临床护理和治疗试验中CD病程和治疗反应性的预测。

项目成果

Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease.

• DOI: 10.1093/ibd/izaa211
• 发表时间: 2020-09
• 期刊: Inflammatory bowel diseases
• 影响因子: 4.9
• 作者: S. Syed;R. Stidham

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.

• DOI: 10.1093/ibd/izab035
• 发表时间: 2021-07-27
• 期刊: Inflammatory bowel diseases
• 影响因子: 4.9
• 作者: Stidham RW;Liu Y;Enchakalody B;Van T;Krishnamurthy V;Su GL;Zhu J;Waljee AK
• 通讯作者: Waljee AK

Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

• DOI: 10.1007/s11894-024-00918-8
• 发表时间: 2024-02
• 期刊: Current gastroenterology reports
• 作者: Mehwish Ahmed;Molly L. Stone;R. Stidham

Effectiveness of Upadacitinib for Patients With Acute Severe Ulcerative Colitis: A Multicenter Experience.

Upadacitinib 对急性重症溃疡性结肠炎患者的疗效：多中心经验。

• DOI: 10.14309/ajg.0000000000002674
• 发表时间: 2024
• 期刊: The American journal of gastroenterology
• 作者: Berinstein,JeffreyA;Karl,Taylor;Patel,Anish;Dolinger,Michael;Barrett,TerrenceA;Ahmed,Waseem;Click,Ben;Steiner,CalenA;Dulaney,David;Levine,Jake;Hassan,SyedAdeel;Perry,Courtney;Flomenhoft,Deborah;Ungaro,RyanC;Berinstein,Ell
• 通讯作者: Berinstein,Ell

From clinical trials to clinical practice: how should we design and evaluate prediction models in the care of IBD?

• DOI: 10.1136/gutjnl-2021-324712
• 发表时间: 2022-06
• 期刊: GUT
• 影响因子: 24.5
• 作者: Stidham, Ryan William;Vickers, Andrew;Singh, Karandeep;Waljee, Akbar K.
• 通讯作者: Waljee, Akbar K.